The effects of multipath are well known in the communications systems. Multipath is the term used to define the secondary signals that are locally induced reflections of a primary signal that enter the receiver in question a fraction of a second later than the direct path signal and, because of the relatively short delay between the original signal and the secondary signal, induce a type of destructive interference that results in some type of impairment to the desired signal. In analog FM band automobile receivers, the effects of multipath create an annoying flutter that causes a loss of intelligibility. In television signals, the impairment is called a "ghost" image. A similar impairment occurs in other forms of analog communication. In digital systems, whether for speech or for data transmission for other purposes, multipath basically adds noise to the desired signal, resulting in either outright errors or, much noisier data. In spread spectrum receivers, the effects of multipath are generally found in the correlators used to achieve signal timing synchronization. In certain receivers, which seek to determine location based on triangulation of range distances determined from time delay measurements made from a constellation of orbiting satellites, the effect of multipath is to induce comparatively large instantaneous errors in the time of arrival measurements which translate into large errors in the indicated positions. Removal of these errors is the subject of much of the work done by previous workers in this field. Previous researchers have sought to deal with the effects of multipath by attempting to estimate the magnitude of the error introduced, and to subtract this error or to otherwise compensate for its effects.
The methods employed to acquire and demodulate data from spread spectrum transmission is well known in the art. See R. E. Ziemer and R. L. Peterson, Digital Communications and Spread Spectrum Systems, Macmillan Publ. Co., New York, 1985, pp. 419-447 for a discussion of acquisitions and demodulation of spread spectrum signals. A spread spectrum GPS receiver must obtain both code and carrier synchronization in order to demodulate the desired data successfully. Issues associated with tracking and accurately demodulating a spread spectrum signal, once the signal is acquired, are discussed in many references on spread spectrum analysis, such as Ziemer and Peterson, op cit.
Signal synchronization is performed using a signal correlator. The correlator constantly compares the incoming signal with a local replica S.sub.d (t) of the desired signal; a microprocessor adjusts a time shift .tau. of the local replica signal until satisfactory agreement is obtained. Because the incoming signal and the local replica signal are substantially identical, a measure of the degree of agreement of these two signals is often referred to as an autocorrelation function. A variety of autocorrelation functions AC(.tau.) are shown in various texts. An autocorrelation function AC(.tau.) is formed according to the prescription ##EQU1## depending upon whether integration or summation of sampled values over a suitable contribution time interval is used to compute the composite signal autocorrelation function. The length T of the contribution time interval used to compute the autocorrelation function in Eq. (1A) or (1B) is often chosen to be N times the chip length .DELTA..tau..sub.chip, where N is a large positive number.
Tracking a composite signal requires maintaining signal synchronization. The peak of the autocorrelation function is rounded, not pointed, due to finite bandwidth effects, so that locating a true peak is difficult. Receiver designers have, therefore, resorted to an "early-minus-late" correlation tracking method, as discussed by W. M. Bowles in "Correlation Tracking," Charles Stark Draper Laboratory, May 1980, by Fenton et al in U.S. Pat. No. 5,101,416, and by Lennen in U.S. Pat. Nos. 5,402,450 and 5,493,588. In the early-minus-late tracking method, a first correlator measures an equivalent autocorrelation function when the local replica signal is shifted to an "early" time shift value .tau.=t.sub..EPSILON. relative to the position (.tau.=tp&gt;t.sub..EPSILON.) of an ideal or punctual replica, and a second correlator measures a second equivalent autocorrelation function when the local replica signal is shifted to a "late" time .tau.=t.sub.L (&gt;tp). By subtracting the late autocorrelation function from the early autocorrelation function, a correlation tracking function or autocorrelation difference function .DELTA.AC(.tau.) with at least one zero-crossing point, corresponding to the autocorrelation function, peak can be developed if the separations of the early and late time shifts from the punctual time shift are chosen to be equal.
If the tracking or time shift variable .tau. for the autocorrelation difference function .DELTA.AC(.tau.) lies to the left (to the right) of the zero crossing point, the system uses the presence of positive (negative) values of .DELTA.AC(.tau.) to increase (decrease) the value of .tau. and drive the system toward the zero crossing point for .DELTA.AC(.tau.). The zero-crossing point is thus easily measured and tracked, and the equivalent peak value and peak location for the autocorrelation function is easily determined. At the zero-crossing point on this doublet-like tracking function, maximum correlation occurs between the incoming signal and the local replica signal. The zero-crossing point represents the best estimate of time shift .tau. for signal synchronization. The internal clock time corresponding to the zero crossing point is a good estimate for time of arrival of an incoming signal at the receiver.
Additive superposition of an equivalent autocorrelation function for the multipath signal (reduced in magnitude and delayed in time) onto the autocorrelation function AC(.tau.) for the desired code signal is a useful model for analyzing the effects of presence of multipath signals, as noted in the Fenton et al patent and in the Lennen patents op. cit. Additive superposition of an additional signal onto the desired incoming signal, during the time period when signal correlation occurs, will distort the desired or direct autocorrelation function AC(.tau.;direct) and produce an altered autocorrelation function AC(.tau.;composite) for the composite signal (direct signal plus multipath signal). Errors in indicated punctual time shift value produce errors in the pseudorange measurements, if these are computed based upon the received signal.
Another useful and equivalent model for analyzing the effects of presence of a multipath signal computes the autocorrelation functions AC(.tau.;x;direct) and AC(.tau.;x;multipath) (x=E,L) for the pure direct signal and the pure multipath signal, forms the differences .DELTA.AC(.tau.;direct) and .DELTA.AC(.tau.;multipath) and adds these two difference functions to obtain the autocorrelation difference function .DELTA.AC(.tau.;composite) for the composite signal.
Previous work in the area of multipath amelioration has focused on two approaches: 1) estimating the effects and compensating for multipath-induced errors, and 2) attempting to limit the effects of the estimated multipath errors. In the Lennen patents, op. cit., both approaches are described. The estimation methods seek to model the distortions in order to reproduce a measured instantaneous autocorrelation function and to create a correction term to subtract from the indicated punctual time. Estimation methods are worthwhile, but can never obtain perfection, wherein all multipath effects are removed, because the multipath signals are constantly varying and corrections can only be done after the fact.
A multipath error limitation method, such as described in the Lennen patents op. cit., operates in the early-minus-late correlation tracking loop with a shorter delay between the early signal and late signal correlators than previous methods had employed. This limitation method reduces the effects of the presence of multipath substantially.
Several workers have disclosed incorporation of training signals, as an additive feature, to try to compensate for time delays and/or multipath signals at a receiver. Examples of these disclosures are Tilk in U.S. Pat. No. 3,852,534, Close in U.S. Pat. No. 3,869,673, Costas in U.S. Pat. No. 4,349,915, Wilkinson in U.S. Pat. No. 4,543,657, Plangger et al in U.S. Pat. No. 4,582,434, Kobo et al in U.S. Pat. No. 4,896,213, Dieterich et al in U.S. Pat. No. 5,065,242, Koo in U.S. Pat. Nos. 5,047,859, 5,111,298 and 5,121,211, and Chan et al in U.S. Pat. No. 5,127,051, Iga et al in U.S. Pat. No. 5,130,799, Roy et al in U.S. Pat. No. 5,361,102, Kennedy et al in U.S. Pat. No. 5,408,685, and Patel et al in U.S. Pat. No. 5,600,380.
In many of the previous methods for multipath amelioration, samples are taken of the incoming direct (desired) signal component and multipath signal component(s), and a conventional autocorrelation functions and autocorrelation difference functions are formed and analyzed. No attempt is made to uniquely identify the presence of a direct signal within an incoming composite signal, or to extract this unique signal. What is needed is a multipath discrimination approach that allows prompt verification of the presence of, and identification of, a direct signal that was originally transmitted and identification of a multipath component that may also be present in the incoming composite signal.